DCFS-Based Online Driving Preferences Learning Approach with Application to Personalized Lane Keeping Controller Design

For the automated vehicles, the user experience on comfort plays an important role for the market acceptance. Generally, for the experienced drivers who already form some certain driving preferences during the longtime driving, they will feel apparent discomfort if the automated vehicles drive very differently from them. Therefore, it is of great significance for comfort driving if the automated vehicles could learn the driving preferences of the users. Fortunately, we enter the era of traffic big data, from the cyber physical system (CPS) perspective, we almost can get whatever data we need to map human drivers from physical space to cyberspace. In this paper, we build a general driving model based on deep convolutional fuzzy systems (DCFS), and design an online driving preferences learning algorithm based on the high-dimensional on-board data. For the verification of the method, we apply this method to design a personalized lane keeping controller (PLKC) with considering the guaranteed stability. Fifteen volunteers participate in the experiments on the Prescan-based simulation platform, and the results show that the PLKC has the online learning ability to the fixed and the time-varying lateral driving preferences.

Automated summary

In this study, a general driving model based on deep convolutional fuzzy systems was built, and an online driving preferences learning algorithm was designed and successfully applied to design a personalized lane keeping controller. Experimental results demonstrate that the controller has the online learning ability for fixed and time-varying lateral driving preferences.